Evolution of Machine Learning
Although often thought of as a new concept, “Machine Learning” has attracted extensive research and interest from the scientific community over the last 50 years. In more recent years, important improvements have been made to algorithms, which coupled with the extensive and fast evolution of computing power and storage allow machines to process large amounts of data.
Despite revolutionary improvements, the base principle of Machine Learning has always remained the same: automatically generating statistics from large amounts of data (“training”) and reusing those statistics on new data; in other words, automating a task without explicitly writing a program.
Being able to build personalized content to engage effectively with each individual consumer is a dream come true for any brand, however doing so without defining clear business goals or without having a clear understanding of how personal data is collected and used, can frustrate your customers and ultimately make them hate you!
“You have my data, figure it out!” According to the book “Marketing to the Entitled Consumer” by Selligent CMO Nick Worth, 48% of people expect brands to use their data to make their experience more personal. However, ask anyone around you about what they think of online Personalization, in general, and they will systematically reply that they hate targeted ads: “Last time, I had a look at this pair of shoes on Brand X’s website, ads for this exact pair have been following me around the web for weeks!”
Retargeting using display ads is the best example of AI-powered real-time Personalization, which generates the most hate and frustration for consumers, leading them to install ad-blockers. Why is that?
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Relevance is Key
Personalization does not work without relevance. If you are in the middle of an interesting article and suddenly see an ad for an item you briefly looked at days before, you are interrupted for something that is most probably irrelevant at the particular time. Not only does this affect the present moment, but you then begin to feel like you are being watched, which alters the way you browse and the links you click on, for fear of being bombarded with targeted ads for days and weeks afterwards.
While this is a problem for consumers (which they often solve by installing ad-block software), it is an even bigger problem for marketers: finding a relevant message with personalized content, which is sent at a suitable time and a channel which makes sense for this specific consumer at this specific time, and to so without coming across as intrusive, is an impossible task for a human to perform.
This is where the recent developments in AI come into play: true 1:1 Personalization across channels (such as email, website, mobile push, SMS, social networks, print) is becoming possible without the effort and investment it used to require a few years back with the use of Machine Learning algorithms and Marketing data. For many modern brands, the intelligent use of Personalization technologies is becoming a strong differentiator in their Marketing Technology strategy as it allows them to create a personalized, relevant experience to each of their customers.
AI Can Foster Long-Lasting Relationships With Consumers
Using AI to serve relevant content is a prerequisite to building long-lasting relationships with your customers. Netflix with their personalized series/movies recommendations, Spotify with their personalized playlists, Facebook and Twitter with their personalized news feeds and most tech giants use real-time recommendation engines to adapt their content (and UI/UX) to each individual person. Personalized recommendations can increase engagement and conversion by adapting the content of your emails, mobile push notifications and website to each individual user using Machine Learning.
This native form of Personalization can increase your conversion rate tremendously and offer a great experience to your customers. However, nowadays, Content Personalization alone is not enough: Serving this content at the right moment, and on the right channel for each individual also has a strong impact on engagement and loyalty. Again, Machine Learning algorithms exploiting your consumer data can help you automate these complex operations and find the best channel and the best time to send a message to a specific user.
The technical aspects related to the science and algorithms used for implementing such features is not something you want to spend time on. These features should be available out-of-the-box in your Marketing cloud, which should constantly collect data and make it actionable on all channels you communicate on.
Adapting content, automating audience segment creation, fine-tuning communication time and channel at scale and finally ensuring that each consumer perceives your communications as relevant and interesting holds the key to keeping customers on side when it comes to AI.
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